Publications

Le, T. et al. (2018) “Auto-encoding sequential Monte Carlo”, in 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.

Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.

Webb, S. et al. (2018) “Faithful inversion of generative models for effective amortized inference”, in Advances in Neural Information Processing Systems.

Rainforth, T. et al. (2018) “Tighter variational bounds are not necessarily better”, in 35th International Conference on Machine Learning, ICML 2018.

Lyddon, S., Walker, S. and Holmes, C. (2018) “Nonparametric learning from Bayesian models with randomized objective functions”, in Advances in Neural Information Processing Systems, pp. 2071–2081.

Ismail, S. et al. (2017) “A two-component copula with links to insurance”, Dependence Modeling, 5(1), pp. 295–303.

Flaxman, S., Teh, Y. and Sejdinovic, D. (2017) “Poisson intensity estimation with reproducing kernels”, Electronic Journal of Statistics, 11(2), pp. 5081–5104.

Maddison, C. et al. (2017) “Filtering variational objectives”, in Advances in Neural Information Processing Systems. Neural Information Processing Systems Foundation.

Perrone, V. et al. (2017) “Poisson random fields for dynamic feature models”, Journal of Machine Learning Research, 18.

Coulson, M., Gaunt, R. and Reinert, G. (2017) “Compound Poisson approximation of subgraph counts in stochastic block models with multiple edges”, arXiv [Preprint].